# VoxDub: A Bilingual Lip-Synchronized Audio-Visual Dataset for Automatic Video Dubbing

arXiv: https://arxiv.org/abs/2610.25259 · HTML: https://mlsanity.com/paper/2610.25259/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Audio-visual speech recognition, one of 11 directions in Speech and audio language models.
- First posted on arXiv 18 Oct 2026.

## Placed in

- Speech and audio language models / [Audio-visual speech recognition](https://mlsanity.com/speech/audio-visual-speech-recognition/)

## Abstract (arXiv)

Automatic video dubbing (AVD) requires temporally aligned speech, transcripts, and video, yet existing corpora vary in audio quality, transcription reliability, and speaking-face visibility. We introduce VoxDub, a bilingual lip-synchronized audio-visual dataset containing 766K clips and approximately 1.58K hours of in-the-wild Chinese and English recordings, covering varied speakers, scenes, speaking styles, and linguistic content. Its curation pipeline combines speech-centered segmentation with dual-ASR transcription checking, speech quality filtering, face visibility checks, and lip-speech synchronization verification. Text-to-speech (TTS) experiments show that training on VoxDub yields lower word error rates and higher speaker similarity than training on the compared audio-visual corpora, LRS3 and CN-CVS. AVD experiments show broadly comparable speech quality and subjective synchronization. These results support the utility of VoxDub for TTS and AVD training. More details are available on the project website (https://voxdub-dataset.github.io/VoxDub/).